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Found 3 papers, 2 papers with code
Language Models are Better Bug Detector Through Code-Pair Classification
We evaluate our task in real-world dataset of bug detection and two most powerful LLMs.
Can LLMs Patch Security Issues?
In this paper, we propose a novel approach, Feedback-Driven Solution Synthesis (FDSS), designed to explore the use of LLMs in receiving feedback from Bandit, which is a static code analysis tool, and then the LLMs generate potential solutions to resolve security vulnerabilities.
Learning Defect Prediction from Unrealistic Data
To investigate this conjecture, we propose an approach for identifying the subsets of these large yet unrealistic datasets that are most similar to examples in real-world datasets based on their learned representations.
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